CORD results for <i>MYOD1</i>.

<p><b>A</b>) The differentiation of muscle stem cells (satellite cells) to myoblasts and ultimately to skeletal muscle is under the control of muscle regulatory factors including the transcription factor MyoD. CORD output for <i>MYOD1</i> demonstrates co- expression of other muscle regulatory factors like myogenin (<i>MYOG</i>) and many genes implicated in muscle differentiation. <b>B</b>) The MyoD1 correlated genes were over representative for several KEGG pathways relating to muscle such as “Cardiac muscle contraction” and “Dilated cardiomyopathy.”</p

The CO-Regulation Database (CORD): A Tool to Identify Coordinately Expressed Genes

<div><p>Background</p><p>Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.</p><p>Results</p><p>Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (<i>CDKN2A</i>), vimentin (<i>VIM)</i>, MyoD (<i>MYOD1</i>). <i>CDKN2A</i>, <i>VIM</i>, and <i>MYOD1</i> all displayed gene correlations consistent with known interacting genes.</p><p>Conclusions</p><p>We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at <a href="http://cord-db.org" target="_blank">http://cord-db.org</a>.</p></div

CORD results for <i>CDKN2A</i>.

<p><b>A</b>) <i>CDKN2A</i> encoding p16 plays a significant role in the cell cycle by regulating the initiation of DNA replication. A simplified diagram shows select genes that play a major role in the cell cycle. CORD identifies many genes known to play major roles in the cell cycle by determining genes co-regulated with <i>CDKN2A</i> (bolded text.) <b>B</b>) The <i>CDKN2A</i>-correlated genes were over representative for several KEGG pathways in cancer and the cell cycle including “DNA replication”, “p53 signaling”, and “cell cycle.”</p

Top 20 Genes Co-expressed with vimentin (<i>VIM</i>) identified by CORD.

<p>Top 20 Genes Co-expressed with vimentin (<i>VIM</i>) identified by CORD.</p

Construction of CORD database using a gene-centric approach.

<p><b>A</b>) A part of the microarray study E-MEXP-3167 from the ArrayExpress database. <b>B</b>) The samples were grouped together by either the Individual or Grouped Factor Method and all the groups were compared to one another. <b>C</b>) The differentially genes for each comparison was determined. Genes with multiple probes were reduced to one entry by averaging the fold change for the multiple probes. If the multiple probes for a gene were differentially regulated in the opposite direction, the gene was removed from the list of differentially expressed genes.</p

Additional file 1: of Impact of a diet and activity health promotion intervention on regional patterns of DNA methylation

Figure S1. Bioinformatics and data analysis pipeline. Table S1. Top 40 differentially regions between control vs. pooled sequential/simultaneous at 3 months. Table S2. Top 40 differentially regions between control vs. pooled sequential/simultaneous at 9 months. (DOCX 710 kb

Determination of co-regulated genes.

<p><b>A</b>) The list of co-regulated genes was determined for each gene using the Individual and Grouped Factor Method. The two gene lists were then compared to one another by determining the % overlap (similarity) of the lists for the top 10 to top 1000 most correlated genes. The % overlap reached a plateau at 47%. <b>B</b>) The first derivative of the % overlap vs. the gene list size shows that on average, after comparing the top 400 genes the lists are no longer similar. <b>C, D</b>) This analysis was repeated for randomly generated gene lists and showed no change in the rate of % overlap vs. gene list size. <b>E</b>) To determine how using the Individual or Grouped Factor method effected gene-gene correlation co-efficients, we analyzed the ratio of the correlation co-efficient for each gene-gene pair. A histogram of this data shows that on average, the Grouped Factor method yielded higher correlation co-efficients.</p

CORD results for <i>VIM</i>.

<p><b>A</b>) The epithelial-to-mesenchymal (EMT) and mesenchymal-to-epithelial transitions are important oncogenic pathways where vimentin (<i>VIM</i>) plays a central role. Twelve of the top 20 most correlated <i>VIM</i> genes affect the EMT transition. <b>B</b>) The EMT transitions depend heavily on cell adhesion. The <i>VIM</i>-correlated genes were over representative for several KEGG pathways in cell adhesion and cancer pathways such as “ECM-receptor interaction”, “Focal adhesion”, and “Pathways in cancer.”</p

Post-Transcriptional Regulation of KLF4 by High-Risk Human Papillomaviruses Is Necessary for the Differentiation-Dependent Viral Life Cycle

<div><p>Human papillomaviruses (HPVs) are epithelial tropic viruses that link their productive life cycles to the differentiation of infected host keratinocytes. A subset of the over 200 HPV types, referred to as high-risk, are the causative agents of most anogenital malignancies. HPVs infect cells in the basal layer, but restrict viral genome amplification, late gene expression, and capsid assembly to highly differentiated cells that are active in the cell cycle. In this study, we demonstrate that HPV proteins regulate the expression and activities of a critical cellular transcription factor, KLF4, through post-transcriptional and post-translational mechanisms. Our studies show that KLF4 regulates differentiation as well as cell cycle progression, and binds to sequences in the upstream regulatory region (URR) to regulate viral transcription in cooperation with Blimp1. KLF4 levels are increased in HPV-positive cells through a post-transcriptional mechanism involving E7-mediated suppression of cellular miR-145, as well as at the post-translational level by E6–directed inhibition of its sumoylation and phosphorylation. The alterations in KLF4 levels and functions results in activation and suppression of a subset of KLF4 target genes, including <i>TCHHL1</i>, <i>VIM</i>, <i>ACTN1</i>, and <i>POT1</i>, that is distinct from that seen in normal keratinocytes. Knockdown of KLF4 with shRNAs in cells that maintain HPV episomes blocked genome amplification and abolished late gene expression upon differentiation. While KLF4 is indispensable for the proliferation and differentiation of normal keratinocytes, it is necessary only for differentiation-associated functions of HPV-positive keratinocytes. Increases in KLF4 levels alone do not appear to be sufficient to explain the effects on proliferation and differentiation of HPV-positive cells indicating that additional modifications are important. KLF4 has also been shown to be a critical regulator of lytic Epstein Barr virus (EBV) replication underscoring the importance of this cellular transcription factor in the life cycles of multiple human cancer viruses.</p></div

KLF4 is required for HPV DNA amplification and late gene expression.

<p>2A. KLF4 was transiently silenced in CIN-612 cells using lentiviral shRNAs. Differentiation was induced by suspending cells in methylcellulose [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005747#ppat.1005747.ref007" target="_blank">7</a>,<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005747#ppat.1005747.ref040" target="_blank">40</a>]. (i) The reductions in KLF4 protein levels were observed by western analysis in both undifferentiated and differentiated conditions of shKLF4 cells compared to mock and shGFP controls. (ii) Silencing KLF4 with shRNAs impaired the ability of the cells to amplify episomal DNA upon differentiation as shown by Southern blot analysis. (iii) Silencing KLF4 also resulted in the reduction in levels of late mRNA transcripts upon differentiation when compared to the controls as shown by northern blot analysis. 2B. KLF4 was stably silenced in HFK-31gen cells using lentiviral shRNAs. Following infection with shRNA lentiviruses, cells were expanded and selected with puromycin. (i) KLF4 protein levels were reduced in cells stably transduced with shKLF4 lentivirus compared to both mock and shGFP lentivirus controls as shown in the western blot. (ii) Stable silencing of KLF4 reduced the amount of viral DNA amplification upon differentiation compared to the controls as shown by Southern blot analysis.</p